Creating digital content from digital scans of real-world objects is a convenient way to produce realistic digital objects. Specifically, digitizing real-world objects involves scanning an object in various static poses using various scanning technologies and reconstructing a three-dimensional digital version of the object. By scanning objects and creating digital versions, content creators can create a library of digital objects. In particular, by using digital scanning, content creators are able to create digital content more quickly, and often more accurately, than by creating the digital objects from scratch.
While scanning techniques allow content creators to create digital versions of real-world objects, conventional scanning techniques have drawbacks. For example, an object, which has joints or other movable connections, includes components that translate and/or rotate with respect to other components of the object. Capturing the simple geometry of such objects is typically not sufficient for recreating a complete structure of the objects in digital form. Without capturing of the functionality (e.g., movable connections) of the various components of the objects, the digital versions may potentially require content creators to modify the digital versions to achieve correct functionality.
Some conventional content digitizing systems use scanning techniques to identify structure and functionality of an object for creating digital models of the object. Specifically, to identify the functionality of objects, some conventional content digitizing systems require animating or re-posing an object to identify movable components of the object and connection types between the movable components. For example, conventional systems capture the functionality using a plurality of digital scans (e.g., by animating a motion of the movable components) to accurately reproduce the object in a digital format. Using animations or large numbers of digital scans to reproduce digital content, however, can require significant amounts of processing power and time. Such conventional systems are also frequently limited in the types of joints or connections that the systems can identify with accuracy.
Additionally, conventional content digitizing systems typically assume ideal conditions (e.g., noiseless digital scans/captured geometries). When introducing noise into the digital scans/captured geometries, the conventional systems are more likely to return erroneous structure and joint estimations. Specifically, conventional systems that perform only sequential analysis across a plurality of digital scans can introduce estimation errors when using noisy digital scans. Thus, conventional systems have difficulty processing noisy digital scans or image captures to obtain an accurate digital model of a real-world object.
These and other disadvantages may exist with respect to conventional content digitization techniques.